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• W4384080488 abstract "In this paper, we propose Hierarchical Federated Learning with Momentum Acceleration (HierMo), a three-tier worker-edge-cloud federated learning algorithm that applies momentum for training acceleration. Momentum is calculated and aggregated in the three tiers. We provide convergence analysis for HierMo, showing a convergence rate of <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$mathcal {O}(frac{1}{T})$</tex-math></inline-formula> . In the analysis, we develop a new approach to characterize model aggregation, momentum aggregation, and their interactions. Based on this result, we prove that HierMo achieves a tighter convergence upper bound compared with HierFAVG without momentum. We also propose HierOPT, which optimizes the aggregation periods (worker-edge and edge-cloud aggregation periods) to minimize the loss given a limited training time. By conducting the experiment, we verify that HierMo outperforms existing mainstream benchmarks under a wide range of settings. In addition, HierOPT can achieve a near-optimal performance when we test HierMo under different aggregation periods." @default.
• W4384080488 created "2023-07-13" @default.
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• W4384080488 date "2023-10-01" @default.
• W4384080488 modified "2023-09-26" @default.
• W4384080488 title "Hierarchical Federated Learning With Momentum Acceleration in Multi-Tier Networks" @default.
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• W4384080488 doi "https://doi.org/10.1109/tpds.2023.3294688" @default.
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